fix: 前面的版本存在的问题: 使用模型时表格没有label列就会报错。本版本修复了该缺陷，解决方案是：在使用模型的时，将数据的缺失label列添加默认值

java_compile/src/main/java/com/example/services/ModelService.java

@@ -283,7 +283,7 @@ public class ModelService {
             * */
                     PipelineModel dtModel = dtClassification.dtTraining(libsvm,
                             Integer.parseInt(modelArguments.get("Category")),
-                            Float.parseFloat(modelArguments.get("TrainingSetOccupy")));
+                            Float.parseFloat(modelArguments.get("TrainingDataSetOccupy")));
                     HashMap<String,String> dtResult = dtClassification.dtResult(dtModel, modelId);
 
                     rsl = updateResOfModel(modelId, dtResult, dtModel);

java_compile/src/main/scala/com/example/data/FitTestData.scala

@@ -1,7 +1,6 @@
 package com.example.data
 
 import java.util
-
 import com.example.SparkConnect
 import com.example.dao._
 import com.example.entity.{Config, Model}
@@ -10,6 +9,7 @@ import org.apache.spark.ml.classification.LogisticRegressionModel
 import org.apache.spark.ml.clustering.KMeansModel
 import org.apache.spark.ml.feature.{StringIndexer, StringIndexerModel}
 import org.apache.spark.ml.{Pipeline, PipelineModel}
+import org.apache.spark.sql.functions.lit
 import org.apache.spark.sql.types.StringType
 import org.apache.spark.sql.{DataFrame, SaveMode}
 import org.springframework.beans.factory.annotation.Autowired
@@ -24,7 +24,7 @@ import scala.collection.JavaConverters._
 class FitTestData extends SparkConnect{
 
   @Autowired
-  val fileLocDao: FileInfoDao = null
+  val fileInfoDao: FileInfoDao = null
 
   @Autowired
   val hdfsDao: HdfsDao = null
@@ -41,10 +41,10 @@ class FitTestData extends SparkConnect{
   @throws(classOf[Exception])
   def transformData(fileId: Int, modelId: Int): util.List[util.Map[String, String]] = {
 
-    val fileLocs = fileLocDao.findById(fileId)
-    println(fileLocs)
-    val filepath = fileLocs.getLocation
-    val filename = fileLocs.getFilename
+    val fileInfo = fileInfoDao.findById(fileId)
+    log.info("fileId=" + fileId + ", read: "+fileInfo.toString)
+    val filepath = fileInfo.getLocation
+    val filename = fileInfo.getFilename
 
     log.info("file loc: " + filepath)
 
@@ -61,7 +61,8 @@ class FitTestData extends SparkConnect{
     //val keyFeature = fieldsId.remove(fieldsId.size() - 1)
     val fieldNames: Array[String] =  new Array[String](fieldsId.size()-1)
 
-    val keyFeature = headerInfoDao.findById(Integer.parseInt(fieldsId.get(fieldsId.size()-1))).getFieldName
+    val labelHeaderInfoId =  fieldsId.get(fieldsId.size()-1)
+    val keyFeature = headerInfoDao.findById(Integer.parseInt(labelHeaderInfoId)).getFieldName
     var i: Int = 0
     var flag = false
     while(iter.hasNext && !flag){
@@ -75,12 +76,34 @@ class FitTestData extends SparkConnect{
     val spark = sparkSession
 
     import spark.implicits._
-    val parquetFile = sparkSession.read.parquet(filepath)
+    val dataFrameFromParquet = sparkSession.read.parquet(filepath)
+
+
+
+    //此处需要做一个适配，若文件此时没有label列，就先加上
+    var dataFrameForPredict:DataFrame = dataFrameFromParquet
+    if(!dataFrameFromParquet.columns.toList.contains(keyFeature)){
+
+      val labelHeaderInfo = headerInfoDao.findById(Integer.parseInt(labelHeaderInfoId))
+      val valueInfo = labelHeaderInfo.getValueInfo
+      val fieldType = labelHeaderInfo.getFieldType
+      val valueArray = json2Object.Json2List(valueInfo)
+      val labelDefaultValueStr = valueArray.get(0)
+
+      val labelDefaultValue = fieldType match {
+        case "string" => labelDefaultValueStr
+        case "int" => Integer.parseInt(labelDefaultValueStr)
+        case "double"  => labelDefaultValueStr.toDouble
+        case _ => log.error("类型出错")
+      }
+
+      dataFrameForPredict = dataFrameFromParquet.withColumn(keyFeature,lit(labelDefaultValue))
+    }
+    //添加部分到此结束
+
 
 
-    //这一段是直接复制的Lib，获得数据时，不需要处理label数据，全部删除
-    parquetFile.select()
-    val featuresDF = parquetFile.select(keyFeature,fieldNames:_*).persist()
+    val featuresDF = dataFrameForPredict.select(keyFeature,fieldNames:_*).persist()
     featuresDF.schema.foreach(println)
 
     val strFeatures = featuresDF.schema.filter(_.dataType.equals(StringType)).map(_.name)